The present invention relates to database search, and more particularly to intelligent search.
In general, a search engine is software that searches for data based on some criteria. This data often resides in databases residing on Web servers, but search techniques similar to search engines may be used in databases residing on local servers. Every Web search engine site uses a search engine that it has either developed itself or has purchased from a third party. Search engines can differ dramatically in the way they find and index material on the Web, and the way they search the indexes from the user's query.
In conventional techniques, search engines use pattern matching methodologies and the web contents are searched by matching user given words. This surface matching technique generates results in millions and billions and most of the time irrelative and unrelated results are shown.
What is needed to increase the relevance of any search is a semantic search, wherein bits of information are categorized and tagged according to their relationship with other data.
Traditional expert systems are designed for centralized and controlled environments with clear taxonomies either controlled and maintained by respected authorities or otherwise generally accepted. They are bipartite models based upon concepts and instances. The ontologies created in such domains as chemistry, medicine or engineering are highly controlled and managed and require experts rather than ignorant users to query them. Such ontologies only work in certain domains due to the many ambiguities of the language. The word “card” may mean something completely different for a poker player and a computer expert.
On the other end, in the past decade very loosely defined, lightweight ontology systems have been formed in the world wide web which are mostly based upon some form of the “friend of a friend” (FOAF) principle. Such systems are in a way tripartite semantic systems extending the bipartite classical semantic system of concepts and instances by the third dimension of the user or actor. In such systems a user buying a book at Amazon® will be presented by a number of choices other customers made who bought the same book. Google®s and Facebook®'s collections of personal data are designed to capitalize on such FOAF data. Such lightweight ontologies require the social engagement of the users and are often referred to as folksonomies (from folks and taxonomies). However, they have little or no formal structure and hence have a low specificity. Formally, such systems as used by delicious.com, Flickr®, and so forth, cannot be considered as vocabularies, the simplest possible form of an ontology on the continuous scale of Smith and Welty.